With the continuous development of network technology, people have higher and higher requirements for network speed. While ensuring the loading speed of websites and APPs, image compression has become more and more important. This article will introduce a Java-based image compression implementation method.
The ImageIO class is used to process images in Java. This class can directly read images and process them. Among them, the write method in the ImageIO class can write the processed image to the specified file, and if the JPEG image compression algorithm is used, the image can be compressed.
The following is how to use Java to achieve image compression:
First, you need to add the following dependencies to the project:
com.github.hesidoryn thumbnailator 0.4.13
This dependency provides the Thumbnailator class, and you can use the methods it provides to achieve operations such as thumbnailing, rotating, and compressing images.
Create a compression method in the code, and use the withSize(int width, int height) method provided by the Thumbnailator class to set the compressed image size , use the outputQuality(double quality) method to set the compression quality (between 0.0-1.0).
The method code is as follows:
public static void compressPic(String srcFilePath, String destFilePath, int width, int height, double quality) throws IOException { File srcFile = new File(srcFilePath); File destFile = new File(destFilePath); Thumbnails.of(srcFile) .size(width, height) .outputQuality(quality) .toFile(destFile); }
Call the compression method in the program and test its effect.
public static void main(String[] args) throws IOException { String srcFilePath = "example.jpg"; String destFilePath = "compressed.jpg"; int width = 500; int height = 500; double quality = 0.75; compressPic(srcFilePath, destFilePath, width, height, quality); }
The above is how to implement image compression using Java. You can adjust the compression quality by increasing or decreasing quality, and adjust the compressed size by increasing or decreasing width and height. This method is simple and easy to use, can process a large number of images, and is suitable for application scenarios of batch processing of images.
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